Integrated Surrounding Monitor and Recognition System
碩士 === 國立中央大學 === 資訊工程學系 === 104 === In recent years, as the security awareness gradually increase and various manufacturers have been introducing new products, the monitoring devices become more prevalent. No matter where you are, you can see these devices, such as communities and buildings. Curren...
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ndltd-TW-104NCU053920982019-05-15T23:01:21Z http://ndltd.ncl.edu.tw/handle/4a5bun Integrated Surrounding Monitor and Recognition System 整合式的全周監視與辨識系統 Shih-Wei Ho 何世偉 碩士 國立中央大學 資訊工程學系 104 In recent years, as the security awareness gradually increase and various manufacturers have been introducing new products, the monitoring devices become more prevalent. No matter where you are, you can see these devices, such as communities and buildings. Currently, most of the monitoring system of the building installation through multiple cameras at each corner and use different angles to achieve the purpose of monitoring the environment, as a result, not only to pay attention to guard image per screen at any time, and there is no concept of a unified space, and the management less convenient. The view angle of a fisheye camera is 180 degree, so it can cover a wider field of view than a normal camera. Thus, in the same surveillance environment, only a few fisheye cameras can replace many traditional cameras to survey the events; such that the cost of system construction and management are then reduced. We use the fisheye camera as our main monitoring device, and propose integrated surrounding monitor and recognition system. The proposed system is composed of two major modules: surrounding monitor and online recognition system. In the surrounding monitor module, we mount the cameras around the building and tilt 25 degrees. According to the relationship between the image plane and the surrounding map, we can solve the homography matrix and point the intruder on the surrounding map. In recognition system, when a foreground object is detected, we extract the foreground object’s feature to recognize whether it’s intruder. If it were an intruder, the system will show alarm message on the screen to notice the user. Through the recognition system we can reduce most of unnecessary human resources. We conducted experiments with the proposed system on several videos. The experiments results show that the average detection rate is 96.5 percent with 583 samples, the recognition rate can up to 93.8 percent with 681 samples and the average false positive rate is 2.53 percent. Din-Chang Tseng 曾定章 2016 學位論文 ; thesis 72 zh-TW |
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碩士 === 國立中央大學 === 資訊工程學系 === 104 === In recent years, as the security awareness gradually increase and various manufacturers have been introducing new products, the monitoring devices become more prevalent. No matter where you are, you can see these devices, such as communities and buildings. Currently, most of the monitoring system of the building installation through multiple cameras at each corner and use different angles to achieve the purpose of monitoring the environment, as a result, not only to pay attention to guard image per screen at any time, and there is no concept of a unified space, and the management less convenient.
The view angle of a fisheye camera is 180 degree, so it can cover a wider field of view than a normal camera. Thus, in the same surveillance environment, only a few fisheye cameras can replace many traditional cameras to survey the events; such that the cost of system construction and management are then reduced. We use the fisheye camera as our main monitoring device, and propose integrated surrounding monitor and recognition system. The proposed system is composed of two major modules: surrounding monitor and online recognition system.
In the surrounding monitor module, we mount the cameras around the building and tilt 25 degrees. According to the relationship between the image plane and the surrounding map, we can solve the homography matrix and point the intruder on the surrounding map.
In recognition system, when a foreground object is detected, we extract the foreground object’s feature to recognize whether it’s intruder. If it were an intruder, the system will show alarm message on the screen to notice the user. Through the recognition system we can reduce most of unnecessary human resources.
We conducted experiments with the proposed system on several videos. The experiments results show that the average detection rate is 96.5 percent with 583 samples, the recognition rate can up to 93.8 percent with 681 samples and the average false positive rate is 2.53 percent.
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author2 |
Din-Chang Tseng |
author_facet |
Din-Chang Tseng Shih-Wei Ho 何世偉 |
author |
Shih-Wei Ho 何世偉 |
spellingShingle |
Shih-Wei Ho 何世偉 Integrated Surrounding Monitor and Recognition System |
author_sort |
Shih-Wei Ho |
title |
Integrated Surrounding Monitor and Recognition System |
title_short |
Integrated Surrounding Monitor and Recognition System |
title_full |
Integrated Surrounding Monitor and Recognition System |
title_fullStr |
Integrated Surrounding Monitor and Recognition System |
title_full_unstemmed |
Integrated Surrounding Monitor and Recognition System |
title_sort |
integrated surrounding monitor and recognition system |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/4a5bun |
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